Artificial Intelligence For Cognitive Behavior Assessment In Children

Abstract

Cognitive impairments in early childhood can lead to poor academic performance and require proper remedial intervention at the appropriate time. ADHD affects about 6-7% of children and is a psychiatric neurodevelopmental disorder that is very hard to diagnose or tell apart from other disorders. Cognitive insufficiencies hinder the development of working memory and can affect school success and even have long term effects that can result in low self-esteem and self-acceptance.

The main aim of this research is to investigate development of an automated and non-intrusive system for assessing physical exercises related to the treatment and diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). A proposed artificial intelligent cognitive behavior assessment system takes advantage of state-of-the-art knowledge from both fields of Computer and Cognitive sciences, and aims to assist therapists in decision making, by providing advanced statistics and sophisticated metrics regarding the subject’s performance. The ultimate goal is to deliver meaningful information to cognitive experts and help develop skills in children that can result in overall improvement of child’s academic performance. To facilitate this, research has been employed in artificial intelligence, computer vision, machine learning and human computer interaction.

Computational methods for human motion analysis are proposed in this dissertation; to provide automatic measurements of various metrics of performance. These are metrics related to generic motion features as well as metrics explicitly defined by experts. To conclude, a novel set of user-interfaces is introduced, specifically designed to assist human experts with data-capturing and motion-analysis, using intuitive and descriptive visualizations.